Exporting Results

Tutorial

Exporting Results

Export query results to CSV, Parquet, DataFrame, and NetworkX

25 min Advanced

ExportCSVParquetNetworkX

What You'll Learn

• Export to CSV - Save query results as comma-separated files
• Export to Parquet - Columnar format for efficient analytics
• Export to DataFrame - Query directly into Polars DataFrames
• Export to NetworkX - Convert results to a Python graph for analysis

# Cell 1 — Parameters
USERNAME = "_FILL_ME_IN_"  # Set your email before running

# Cell 2 — Connect
from graph_olap import GraphOLAPClient
client = GraphOLAPClient(username=USERNAME)

# Cell 3 — Provision
from notebook_setup import provision
personas, conn = provision(USERNAME)
analyst = personas["analyst"]
admin = personas["admin"]
ops = personas["ops"]
client = analyst

print(f"Connected | {conn.query_scalar('MATCH (n) RETURN count(n)')} nodes")

1

Export to CSV

Save query results as a CSV file

# Run a query and export results to CSV
result = conn.query("MATCH (c:Customer) RETURN c.id LIMIT 10")
result.to_csv("customers.csv")
print("Exported to customers.csv")

# Preview what was written
import pathlib
print(pathlib.Path("customers.csv").read_text()[:500])

2

Export to Parquet

Columnar format for analytics workloads

# Parquet is ideal for large exports — smaller files, faster reads
result = conn.query("""
    MATCH (a:Customer)-[:SHARES_ACCOUNT]->(b:Customer)
    RETURN a.id AS from_customer, b.id AS to_customer
    LIMIT 20
""")
result.to_parquet("customer_accounts.parquet")
print("Exported to customer_accounts.parquet")

import os
size_kb = os.path.getsize("customer_accounts.parquet") / 1024
print(f"File size: {size_kb:.1f} KB")

3

Export to DataFrame

Query directly into a Polars DataFrame

# query_df() returns a Polars DataFrame directly — no intermediate step
df = conn.query_df("""
    MATCH (c:Customer)
    RETURN c.id AS name
    LIMIT 10
""")
df

4

Export to NetworkX

Convert results to a Python graph for analysis

# Convert query results to a NetworkX graph for Python-native analysis
result = conn.query("""
    MATCH (a:Customer)-[r:SHARES_ACCOUNT]->(b:Customer)
    RETURN a, r, b
    LIMIT 50
""")

G = result.to_networkx()
print(f"NetworkX graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges")
print(f"Node types: {set(dict(G.nodes(data='label', default='unknown')).values())}")

Key Takeaways

• Use result.to_csv(path) for simple, portable exports
• Use result.to_parquet(path) for compact, columnar storage
• Use conn.query_df(cypher) for direct Polars DataFrame access
• Use result.to_networkx() to build a Python graph for algorithms and visualization
• All exports work on query results — there is no separate export resource